Modeling Healthcare Policy: From Calibration to Optimization

Abstract

Setting effective healthcare policy is complex, particularly due to the dynamic and heterogeneous nature of individual behaviors. While modeling studies offer valuable insights, their computational complexity and calibration requirements can limit practical applications. An important question when identifying optimal healthcare policies is how to consider heterogeneous individual health behavior dynamics while at the same time find ways to consume less time and computational resources. This dissertation addresses three main objectives. The first objective aims to optimize public health interventions to minimize the disease burden of the COVID-19 pandemic. An agent-based model (ABM) is developed to evaluate non-pharmaceutical interventions (NPIs), and vaccination policies under continuous virus mutation. This ABM simulates a heterogeneous population and offers flexibility for addressing diverse policy questions. By addressing parameter uncertainty through calibration and simulating multiple scenarios, the model identifies robust strategies, such as periodic vaccination and adaptive social distancing, for effective disease control. The second objective is to design optimal vaccination promotion campaigns that increase vaccination uptake and improve public health. This approach integrates coupled dynamics, social contagion, and evolutionary game theory to model how vaccination behavior shifts within an ongoing epidemic and with word-of-mouth vaccination campaigns. This model overcomes the limitations of prior studies that assumed static vaccination willingness in vaccination allocation problem. The study offers population-level insights into how resources and messaging should be targeted across demographic groups while considering the societal contexts surrounding vaccination within communities. The final objective is to enhance calibration practices for simulation models by introducing a representative calibration framework. This approach is particularly valuable for complex models where uncertainties exist, and evaluation for policy analysis is computationally expensive. This framework identifies minimal sets of parameter values to limit computational expense while capturing diverse model behaviors. By focusing on representativeness in calibration, this research yields reliable implications for real-world decision-making, filling a gap where other methods emphasize precision at the risk of not accounting for data and model uncertainty. Overall, this dissertation advances healthcare policy modeling by addressing complex and heterogeneous individual behaviors, as well as addressing uncertainties arising from data limitations and simulation complexity through calibration. This research will equip healthcare policymakers to derive informed, data-driven insights from modeling studies, despite model uncertainties and complexities.